Design of a smart biomarker for bioremediation: A machine learning approach

P. T. Krishna Kumar, P. T. Vinod, Vir V. Phoha, S. S. Iyengar, Puneeth Iyengar

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Many trace elements (TE) occur naturally in marine environments and accomplish decisive functions in humans to maintain good health. Mytilus galloprovincialis (MG) is a rich source of TE, but since it is grown near industrial outfalls, they become polluted with elevated levels of TE concentration and serve as biomarkers of pollution. As bioremediation is increasingly reliant on machine learning data processing techniques, we propose the information theoretic concept of using MG for bioremediation. The in situ bioremediation in MG is accomplished by reduction in concentration of TE by the technique of determinant inequalities and the maximization of Mutual Information (MI) without adding any chemical element externally. We bring out the superiority of our technique of MI over that of Principal Component Analysis (PCA) in predicting lower concentration for bioremediation of Cd and Pb in MG.

Original languageEnglish (US)
Pages (from-to)357-360
Number of pages4
JournalComputers in Biology and Medicine
Volume41
Issue number6
DOIs
StatePublished - Jun 2011

Keywords

  • Biomarker
  • Covariance matrix
  • Information theory
  • Maximization of Mutual Information for bioremediation
  • Mytilus galloprovincialis
  • Principal Component Analysis
  • Reduction of trace element concentration for bioremediation
  • Technique of determinant inequalities

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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